dcarpintero/pangolin-guard-large

pangolin-large

This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0225
• F1: 0.9904
• Accuracy: 0.9937

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 64
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
0.1519	0.1042	100	0.1354	0.9229	0.9534
0.068	0.2083	200	0.0553	0.9689	0.9797
0.0458	0.3125	300	0.0555	0.9758	0.9844
0.0389	0.4167	400	0.0442	0.9804	0.9874
0.04	0.5208	500	0.0323	0.9842	0.9897
0.0308	0.625	600	0.0357	0.9836	0.9894
0.0357	0.7292	700	0.0336	0.9861	0.9909
0.0306	0.8333	800	0.0299	0.9880	0.9921
0.0246	0.9375	900	0.0338	0.9846	0.9900
0.0195	1.0417	1000	0.0260	0.9881	0.9922
0.0124	1.1458	1100	0.0225	0.9887	0.9926
0.005	1.25	1200	0.0286	0.9874	0.9917
0.0075	1.3542	1300	0.0313	0.9897	0.9933
0.0065	1.4583	1400	0.0318	0.9892	0.9930
0.0093	1.5625	1500	0.0257	0.9903	0.9937
0.0099	1.6667	1600	0.0233	0.9889	0.9927
0.0054	1.7708	1700	0.0221	0.9905	0.9938
0.0077	1.875	1800	0.0222	0.9907	0.9939
0.0052	1.9792	1900	0.0225	0.9904	0.9937

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0